Path: utzoo!attcan!uunet!cs.utexas.edu!swrinde!ucsd!pacbell.com!lll-winken!tristan!loren
From: loren@tristan.llnl.gov (Loren Petrich)
Newsgroups: comp.ai
Subject: Re: What Has Traditional AI Accomplished?
Message-ID: <70159@lll-winken.LLNL.GOV>
Date: 22 Oct 90 16:58:20 GMT
References: <1990Oct16.135631.6444@cbnewsj.att.com> <69929@lll-winken.LLNL.GOV> <3740@media-lab.MEDIA.MIT.EDU>
Sender: usenet@lll-winken.LLNL.GOV
Organization: Lawrence Livermore National Laboratory
Lines: 39

In article <3740@media-lab.MEDIA.MIT.EDU> minsky@media-lab.media.mit.edu (Marvin Minsky) writes:
>	[that I should read _Perceptrons_...]

	I found the book in the Berkeley library and tried reading it.
It was rather difficult to follow its arguments. I guess I should
check it out and work through it VERY carefully.

	But the impression I get, rightly or wrongly, is that its
principal conclusions are:

One layer of perceptron units can only distinguish between classes of
inputs separated by a hyperplane.

More layers of perceptron units can distinguish between much more
general classes of inputs.

There exist learning rules for one layer of perceptron units, but
there do not appear to be practical learning rules for more than one
layer.


	And that was seemingly that for perceptron-like architectures.


	I guess some algorithm like back-propagation looks simple --
after one discovers it. But it does seem easy to generalize the
two-state output of the original perceptrons to a continuous-valued
output, from which the back-prop algorithm readily follows from
minimizing the quantity <|actual - calculated|>. I wonder if anyone
had ever considered continuous-output perceptrons in the early days of
the field.


$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$
Loren Petrich, the Master Blaster: loren@sunlight.llnl.gov

Since this nodename is not widely known, you may have to try:

loren%sunlight.llnl.gov@star.stanford.edu